Overview

Dataset statistics

Number of variables48
Number of observations14829
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory377.0 B

Variable types

Numeric7
Boolean1
Categorical40

Alerts

display_1 has constant value "0"Constant
display_A has constant value "0"Constant
Unnamed: 0 is highly overall correlated with homeowner_Probable OwnerHigh correlation
pct_disc is highly overall correlated with pct_retail_discHigh correlation
pct_retail_disc is highly overall correlated with pct_discHigh correlation
marital_status_A is highly overall correlated with hhsize_ordinalHigh correlation
homeowner_Probable Owner is highly overall correlated with Unnamed: 0High correlation
hhcomp_1 Adult Kids is highly overall correlated with kid_category_None/UnknownHigh correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_None/Unknown and 1 other fieldsHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with hhsize_ordinalHigh correlation
hhcomp_Single Female is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_1 is highly overall correlated with kid_category_None/Unknown and 1 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_3+ is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_1 Adult Kids and 3 other fieldsHigh correlation
hhsize_ordinal is highly overall correlated with marital_status_A and 7 other fieldsHigh correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_15.0 is highly overall correlated with description_TypeCHigh correlation
campaign_24.0 is highly overall correlated with description_TypeBHigh correlation
description_TypeA is highly overall correlated with campaign_13.0High correlation
description_TypeB is highly overall correlated with campaign_24.0High correlation
description_TypeC is highly overall correlated with campaign_15.0High correlation
display_2 is highly imbalanced (99.9%)Imbalance
display_3 is highly imbalanced (98.2%)Imbalance
display_4 is highly imbalanced (99.8%)Imbalance
display_5 is highly imbalanced (96.0%)Imbalance
display_6 is highly imbalanced (99.8%)Imbalance
display_7 is highly imbalanced (99.6%)Imbalance
display_9 is highly imbalanced (96.6%)Imbalance
mailer_A is highly imbalanced (86.6%)Imbalance
mailer_C is highly imbalanced (99.8%)Imbalance
mailer_F is highly imbalanced (99.6%)Imbalance
mailer_H is highly imbalanced (95.6%)Imbalance
mailer_L is highly imbalanced (99.6%)Imbalance
homeowner_Probable Owner is highly imbalanced (76.0%)Imbalance
homeowner_Probable Renter is highly imbalanced (85.2%)Imbalance
homeowner_Renter is highly imbalanced (58.0%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (55.7%)Imbalance
hhcomp_Single Male is highly imbalanced (70.0%)Imbalance
kid_category_2 is highly imbalanced (62.4%)Imbalance
kid_category_3+ is highly imbalanced (70.2%)Imbalance
campaign_8.0 is highly imbalanced (94.7%)Imbalance
campaign_13.0 is highly imbalanced (84.0%)Imbalance
campaign_15.0 is highly imbalanced (89.7%)Imbalance
campaign_18.0 is highly imbalanced (94.5%)Imbalance
campaign_24.0 is highly imbalanced (78.3%)Imbalance
campaign_26.0 is highly imbalanced (96.5%)Imbalance
campaign_30.0 is highly imbalanced (96.4%)Imbalance
description_TypeA is highly imbalanced (74.3%)Imbalance
description_TypeB is highly imbalanced (78.3%)Imbalance
description_TypeC is highly imbalanced (89.7%)Imbalance
Unnamed: 0 has unique valuesUnique
pct_disc has 6582 (44.4%) zerosZeros
pct_retail_disc has 6726 (45.4%) zerosZeros
pct_coupon_disc has 14386 (97.0%) zerosZeros

Reproduction

Analysis started2023-05-28 08:58:44.357880
Analysis finished2023-05-28 08:59:08.196186
Duration23.84 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct14829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11194.865
Minimum25
Maximum22226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:08.384223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile1286.4
Q15333
median11262
Q317125
95-th percentile21112.6
Maximum22226
Range22201
Interquartile range (IQR)11792

Descriptive statistics

Standard deviation6516.8553
Coefficient of variation (CV)0.58212898
Kurtosis-1.2554805
Mean11194.865
Median Absolute Deviation (MAD)5896
Skewness-0.0051061725
Sum1.6600865 × 108
Variance42469403
MonotonicityStrictly increasing
2023-05-28T10:59:08.592315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1
 
< 0.1%
15381 1
 
< 0.1%
15383 1
 
< 0.1%
15384 1
 
< 0.1%
15385 1
 
< 0.1%
15386 1
 
< 0.1%
15387 1
 
< 0.1%
15388 1
 
< 0.1%
15389 1
 
< 0.1%
15390 1
 
< 0.1%
Other values (14819) 14819
99.9%
ValueCountFrequency (%)
25 1
< 0.1%
26 1
< 0.1%
27 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
32 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
ValueCountFrequency (%)
22226 1
< 0.1%
22225 1
< 0.1%
22224 1
< 0.1%
22223 1
< 0.1%
22222 1
< 0.1%
22221 1
< 0.1%
22220 1
< 0.1%
22219 1
< 0.1%
22218 1
< 0.1%
22217 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
False
10163 
True
4666 
ValueCountFrequency (%)
False 10163
68.5%
True 4666
31.5%
2023-05-28T10:59:08.816383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct278
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8103436
Minimum0.12
Maximum29.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:08.944404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.25
Q10.5
median0.69
Q33.59
95-th percentile11.99
Maximum29.99
Range29.87
Interquartile range (IQR)3.09

Descriptive statistics

Standard deviation4.3149551
Coefficient of variation (CV)1.5353835
Kurtosis9.2495178
Mean2.8103436
Median Absolute Deviation (MAD)0.4
Skewness2.7417866
Sum41674.585
Variance18.618838
MonotonicityNot monotonic
2023-05-28T10:59:09.092442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 2847
 
19.2%
0.25 972
 
6.6%
0.69 766
 
5.2%
0.79 370
 
2.5%
0.89 313
 
2.1%
2.99 282
 
1.9%
0.33 273
 
1.8%
0.59 264
 
1.8%
0.24 259
 
1.7%
0.39 251
 
1.7%
Other values (268) 8232
55.5%
ValueCountFrequency (%)
0.12 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 6
 
< 0.1%
0.2 11
 
0.1%
0.24 259
 
1.7%
0.25 972
6.6%
0.27 59
 
0.4%
0.28 45
 
0.3%
0.28 157
 
1.1%
0.29 64
 
0.4%
ValueCountFrequency (%)
29.99 36
0.2%
29.79 24
0.2%
28.99 1
 
< 0.1%
26.99 1
 
< 0.1%
25.99 6
 
< 0.1%
21.99 34
0.2%
20.99 6
 
< 0.1%
20.89 12
 
0.1%
20.69 3
 
< 0.1%
20.49 11
 
0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct778
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1295983
Minimum0
Maximum0.98657718
Zeros6582
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:09.240690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.090909091
Q30.24444444
95-th percentile0.37106918
Maximum0.98657718
Range0.98657718
Interquartile range (IQR)0.24444444

Descriptive statistics

Standard deviation0.14630139
Coefficient of variation (CV)1.1288835
Kurtosis0.68616695
Mean0.1295983
Median Absolute Deviation (MAD)0.090909091
Skewness0.9536825
Sum1921.8132
Variance0.021404096
MonotonicityNot monotonic
2023-05-28T10:59:09.404861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6582
44.4%
0.2753623188 497
 
3.4%
0.34 402
 
2.7%
0.22 383
 
2.6%
0.358974359 237
 
1.6%
0.32 195
 
1.3%
0.3333333333 184
 
1.2%
0.1265822785 181
 
1.2%
0.4 145
 
1.0%
0.1304347826 132
 
0.9%
Other values (768) 5891
39.7%
ValueCountFrequency (%)
0 6582
44.4%
0.002222222222 1
 
< 0.1%
0.004282655246 1
 
< 0.1%
0.005 1
 
< 0.1%
0.005617977528 2
 
< 0.1%
0.006734006734 1
 
< 0.1%
0.007054673721 1
 
< 0.1%
0.008403361345 1
 
< 0.1%
0.008403361345 29
 
0.2%
0.008739076155 1
 
< 0.1%
ValueCountFrequency (%)
0.9865771812 1
 
< 0.1%
0.9485094851 1
 
< 0.1%
0.9090909091 3
< 0.1%
0.9036144578 1
 
< 0.1%
0.8866666667 1
 
< 0.1%
0.8823529412 1
 
< 0.1%
0.863574352 1
 
< 0.1%
0.8567335244 1
 
< 0.1%
0.8474576271 1
 
< 0.1%
0.8416886544 1
 
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct585
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12222027
Minimum-0
Maximum0.98657718
Zeros6726
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:09.541050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q10
median0.076982294
Q30.23255814
95-th percentile0.35897436
Maximum0.98657718
Range0.98657718
Interquartile range (IQR)0.23255814

Descriptive statistics

Standard deviation0.13654426
Coefficient of variation (CV)1.1171982
Kurtosis-0.3534032
Mean0.12222027
Median Absolute Deviation (MAD)0.076982294
Skewness0.74341416
Sum1812.4043
Variance0.018644334
MonotonicityNot monotonic
2023-05-28T10:59:09.669072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 6726
45.4%
0.2753623188 529
 
3.6%
0.22 466
 
3.1%
0.34 402
 
2.7%
0.358974359 251
 
1.7%
0.32 195
 
1.3%
0.3333333333 183
 
1.2%
0.1265822785 183
 
1.2%
0.1304347826 179
 
1.2%
0.4 123
 
0.8%
Other values (575) 5592
37.7%
ValueCountFrequency (%)
-0 6726
45.4%
0.002222222222 1
 
< 0.1%
0.004282655246 1
 
< 0.1%
0.005 1
 
< 0.1%
0.005617977528 2
 
< 0.1%
0.006734006734 1
 
< 0.1%
0.007054673721 1
 
< 0.1%
0.008403361345 30
 
0.2%
0.008739076155 1
 
< 0.1%
0.01006711409 1
 
< 0.1%
ValueCountFrequency (%)
0.9865771812 1
 
< 0.1%
0.9090909091 3
< 0.1%
0.8823529412 1
 
< 0.1%
0.8275 1
 
< 0.1%
0.75 6
< 0.1%
0.6060606061 1
 
< 0.1%
0.5459508644 1
 
< 0.1%
0.5420054201 2
 
< 0.1%
0.5020080321 3
< 0.1%
0.5017301038 1
 
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct152
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0073780361
Minimum-0
Maximum0.94850949
Zeros14386
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:09.805335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q10
median0
Q30
95-th percentile-0
Maximum0.94850949
Range0.94850949
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.050267021
Coefficient of variation (CV)6.8130625
Kurtosis94.956913
Mean0.0073780361
Median Absolute Deviation (MAD)0
Skewness8.8566648
Sum109.4089
Variance0.0025267734
MonotonicityNot monotonic
2023-05-28T10:59:09.933357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 14386
97.0%
0.2227171492 18
 
0.1%
0.2386634845 18
 
0.1%
0.2865329513 16
 
0.1%
0.278551532 16
 
0.1%
0.0834028357 15
 
0.1%
0.139275766 14
 
0.1%
0.16 14
 
0.1%
0.3344481605 12
 
0.1%
0.2004008016 11
 
0.1%
Other values (142) 309
 
2.1%
ValueCountFrequency (%)
-0 14386
97.0%
0.005297233667 1
 
< 0.1%
0.02000666889 1
 
< 0.1%
0.03334444815 1
 
< 0.1%
0.03356831151 1
 
< 0.1%
0.04547521601 1
 
< 0.1%
0.04786979416 2
 
< 0.1%
0.05408328826 2
 
< 0.1%
0.05885815185 3
 
< 0.1%
0.0652173913 1
 
< 0.1%
ValueCountFrequency (%)
0.9485094851 1
 
< 0.1%
0.8474576271 1
 
< 0.1%
0.8403361345 4
< 0.1%
0.8016032064 1
 
< 0.1%
0.7194244604 1
 
< 0.1%
0.6787148594 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
0.6435272045 1
 
< 0.1%
0.6329113924 1
 
< 0.1%
0.6329113924 1
 
< 0.1%

display_1
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14829
100.0%

Length

2023-05-28T10:59:10.041513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:10.133761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14829
100.0%

Most occurring characters

ValueCountFrequency (%)
0 14829
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14829
100.0%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14828 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Length

2023-05-28T10:59:10.213791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:10.309983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14804 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Length

2023-05-28T10:59:10.389992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:10.486013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14827 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Length

2023-05-28T10:59:10.566083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:10.662273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14765 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Length

2023-05-28T10:59:10.746341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:10.838503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14827 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Length

2023-05-28T10:59:10.942682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:11.092914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14825 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Length

2023-05-28T10:59:11.215137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:11.319196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14777 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Length

2023-05-28T10:59:11.411265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:11.511451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

display_A
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14829
100.0%

Length

2023-05-28T10:59:11.595429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:11.711528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14829
100.0%

Most occurring characters

ValueCountFrequency (%)
0 14829
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14829
100.0%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14551 
1
 
278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Length

2023-05-28T10:59:11.839696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:11.955755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14827 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Length

2023-05-28T10:59:12.043958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:12.224087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

mailer_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14825 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Length

2023-05-28T10:59:12.344110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:12.464168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14758 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Length

2023-05-28T10:59:13.024597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:13.112811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

mailer_L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14824 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Length

2023-05-28T10:59:13.188860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:13.340916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
1
7420 
0
7409 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Length

2023-05-28T10:59:13.445071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:13.537098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring characters

ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12921 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Length

2023-05-28T10:59:13.621165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:13.713408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
1
9444 
0
5385 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Length

2023-05-28T10:59:13.795509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:13.885568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring characters

ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

homeowner_Probable Owner
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14243 
1
 
586

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Length

2023-05-28T10:59:13.969817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:14.061840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14514 
1
 
315

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Length

2023-05-28T10:59:14.137900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:14.230094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13566 
1
 
1263

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Length

2023-05-28T10:59:14.310287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:14.406305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

hhcomp_1 Adult Kids
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13466 
1
1363 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Length

2023-05-28T10:59:14.486312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:14.582323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12188 
1
2641 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Length

2023-05-28T10:59:14.666348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:14.758497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
9309 
1
5520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Length

2023-05-28T10:59:14.842527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:14.938548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring characters

ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
11852 
1
2977 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Length

2023-05-28T10:59:15.022568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:15.118631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14040 
1
 
789

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Length

2023-05-28T10:59:15.202648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:15.298668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12423 
1
2406 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Length

2023-05-28T10:59:15.382674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:15.478693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13751 
1
 
1078

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Length

2023-05-28T10:59:15.558712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:15.654816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

kid_category_3+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14048 
1
 
781

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Length

2023-05-28T10:59:15.734841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:15.834871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
1
10564 
0
4265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Length

2023-05-28T10:59:15.914889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:16.031003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring characters

ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

age_ordinal
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4965271
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:16.119071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1164063
Coefficient of variation (CV)0.31929005
Kurtosis0.29053601
Mean3.4965271
Median Absolute Deviation (MAD)1
Skewness-0.008886467
Sum51850
Variance1.246363
MonotonicityNot monotonic
2023-05-28T10:59:16.223099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6141
41.4%
3 4461
30.1%
2 1739
 
11.7%
5 908
 
6.1%
6 861
 
5.8%
1 719
 
4.8%
ValueCountFrequency (%)
1 719
 
4.8%
2 1739
 
11.7%
3 4461
30.1%
4 6141
41.4%
5 908
 
6.1%
6 861
 
5.8%
ValueCountFrequency (%)
6 861
 
5.8%
5 908
 
6.1%
4 6141
41.4%
3 4461
30.1%
2 1739
 
11.7%
1 719
 
4.8%

income_ordinal
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.537427
Minimum10
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:59:16.331123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q139.5
median62
Q387
95-th percentile162
Maximum250
Range240
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation44.432845
Coefficient of variation (CV)0.67797665
Kurtosis2.4145352
Mean65.537427
Median Absolute Deviation (MAD)25
Skewness1.4832917
Sum971854.5
Variance1974.2777
MonotonicityNot monotonic
2023-05-28T10:59:16.455316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
62 3767
25.4%
39.5 3178
21.4%
87 2549
17.2%
29.5 1659
11.2%
162 1021
 
6.9%
19.5 873
 
5.9%
10 812
 
5.5%
137 570
 
3.8%
112 152
 
1.0%
250 111
 
0.7%
Other values (2) 137
 
0.9%
ValueCountFrequency (%)
10 812
 
5.5%
19.5 873
 
5.9%
29.5 1659
11.2%
39.5 3178
21.4%
62 3767
25.4%
87 2549
17.2%
112 152
 
1.0%
137 570
 
3.8%
162 1021
 
6.9%
187 56
 
0.4%
ValueCountFrequency (%)
250 111
 
0.7%
224.5 81
 
0.5%
187 56
 
0.4%
162 1021
 
6.9%
137 570
 
3.8%
112 152
 
1.0%
87 2549
17.2%
62 3767
25.4%
39.5 3178
21.4%
29.5 1659
11.2%

hhsize_ordinal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
2.0
6638 
1.0
4215 
3.0
2578 
5.0
747 
4.0
 
651

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44487
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 6638
44.8%
1.0 4215
28.4%
3.0 2578
 
17.4%
5.0 747
 
5.0%
4.0 651
 
4.4%

Length

2023-05-28T10:59:16.575398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:16.687469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 6638
44.8%
1.0 4215
28.4%
3.0 2578
 
17.4%
5.0 747
 
5.0%
4.0 651
 
4.4%

Most occurring characters

ValueCountFrequency (%)
. 14829
33.3%
0 14829
33.3%
2 6638
14.9%
1 4215
 
9.5%
3 2578
 
5.8%
5 747
 
1.7%
4 651
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29658
66.7%
Other Punctuation 14829
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14829
50.0%
2 6638
22.4%
1 4215
 
14.2%
3 2578
 
8.7%
5 747
 
2.5%
4 651
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 14829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44487
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 14829
33.3%
0 14829
33.3%
2 6638
14.9%
1 4215
 
9.5%
3 2578
 
5.8%
5 747
 
1.7%
4 651
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44487
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 14829
33.3%
0 14829
33.3%
2 6638
14.9%
1 4215
 
9.5%
3 2578
 
5.8%
5 747
 
1.7%
4 651
 
1.5%

campaign_8.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14740 
1
 
89

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Length

2023-05-28T10:59:16.815567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:16.947672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14482 
1
 
347

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Length

2023-05-28T10:59:17.047739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:17.151886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

campaign_15.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14629 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Length

2023-05-28T10:59:17.236090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:17.344166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

campaign_18.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14735 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Length

2023-05-28T10:59:17.444149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:17.556217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

campaign_24.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14315 
1
 
514

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Length

2023-05-28T10:59:17.640379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:17.740491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

campaign_26.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14774 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Length

2023-05-28T10:59:17.820515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:17.916580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14773 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Length

2023-05-28T10:59:17.998542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:18.092901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

description_TypeA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14188 
1
 
641

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Length

2023-05-28T10:59:18.172922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:18.289169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

description_TypeB
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14315 
1
 
514

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Length

2023-05-28T10:59:18.393172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:18.525249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

description_TypeC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14629 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Length

2023-05-28T10:59:18.645299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:59:18.773349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Interactions

2023-05-28T10:59:04.518722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:58.474947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.355696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:00.208246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.405202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.333942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:03.386357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:04.686757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:58.595179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.467691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:00.328280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.521219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.461965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:03.530412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:04.838819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:58.723239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.595748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:00.444525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.657243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.622153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:03.682451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:04.994907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:58.851472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.715814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:00.568603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.805323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.770172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:03.850497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:05.158945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:58.987451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.836020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:00.686793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.941419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.922228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:04.030567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:05.319005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.111474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.959983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.161108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.061552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:03.074254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:04.194638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:05.491087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:59.239596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:00.080049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:01.281163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:02.181687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:03.226321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:59:04.342672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-28T10:59:18.937425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discage_ordinalincome_ordinalfirst_purchasedisplay_2display_3display_4display_5display_6display_7display_9mailer_Amailer_Cmailer_Fmailer_Hmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownhhsize_ordinalcampaign_8.0campaign_13.0campaign_15.0campaign_18.0campaign_24.0campaign_26.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
Unnamed: 01.0000.090-0.047-0.0500.0070.0920.0910.1540.0040.0000.0260.0130.0000.0000.1100.0290.0000.0090.0420.0220.3000.2070.3290.5280.2990.3120.3470.2980.3420.2750.1670.2940.3150.1750.2950.2710.0240.0870.1680.0490.0790.0790.0630.0960.0790.168
shelf_price0.0901.0000.1420.1160.168-0.0170.1290.1850.0810.1210.0120.1280.0610.0300.2650.1040.2410.0540.0670.0950.0750.0320.1120.1030.0520.0870.1230.1230.0680.0700.0500.0390.0210.1220.0460.0800.0830.0830.0640.1140.0280.0300.0290.0930.0280.064
pct_disc-0.0470.1421.0000.9730.233-0.052-0.0180.0910.0000.0980.0000.0770.0000.0000.0480.2360.1880.0400.0640.0000.1060.0300.1500.1970.1040.1650.1620.1010.0870.0920.0390.0720.0110.0560.0420.0660.0400.1860.0990.0960.0630.0550.0550.1160.0630.099
pct_retail_disc-0.0500.1160.9731.0000.040-0.059-0.0330.0760.0000.0660.0000.0780.0000.0000.0470.2500.0230.0410.0640.0000.1030.0470.1480.2000.1010.1640.1630.0930.0870.0930.0780.0710.0190.0490.0410.0650.0330.1950.0980.0960.0460.0530.1120.1200.0460.098
pct_coupon_disc0.0070.1680.2330.0401.0000.0150.0660.0840.0000.0450.0000.0150.0000.0000.0000.0230.1260.0000.0000.0000.0430.0120.0590.0190.0410.0360.0380.0590.0440.0300.0020.0090.0270.0350.0280.0350.0220.0000.0000.0280.1030.0000.0000.0000.1030.000
age_ordinal0.092-0.017-0.052-0.0590.0151.0000.1880.0740.0000.0000.0000.0000.0000.0110.0460.0230.0000.0000.0200.0060.1710.2700.3150.2840.1050.2600.2890.2660.2300.1710.2670.2740.3050.2020.3150.1840.0780.0630.2000.0200.0740.0760.0900.0740.0740.200
income_ordinal0.0910.129-0.018-0.0330.0660.1881.0000.1200.1080.0180.0000.0110.0630.0000.0660.0160.0000.0090.0240.0000.3390.2260.4570.2980.2490.2670.3340.3430.3920.2860.2180.2700.1820.1350.2940.2140.0390.0330.0800.0540.0720.0480.0620.0660.0720.080
first_purchase0.1540.1850.0910.0760.0840.0740.1201.0000.0000.0100.0070.0240.0000.0000.0230.0050.0000.0000.0000.0000.0760.0170.0180.0480.0050.0880.0650.0800.0330.0310.0530.0120.0000.0840.0260.1140.0000.0460.0240.0040.0560.0170.0170.0420.0560.024
display_20.0040.0810.0000.0000.0000.0000.1080.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_30.0000.1210.0980.0660.0450.0000.0180.0100.0001.0000.0000.0000.0000.0000.0000.0730.0000.0000.0040.0430.0000.0000.0000.0000.0000.0050.0060.0000.0000.0060.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0120.000
display_40.0260.0120.0000.0000.0000.0000.0000.0070.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_50.0130.1280.0770.0780.0150.0000.0110.0240.0000.0000.0001.0000.0000.0000.0000.0780.0000.0000.0000.0260.0000.0000.0240.0070.0000.0160.0020.0230.0000.0030.0000.0000.0000.0320.0110.0340.0120.0000.0000.0000.0000.0000.0000.0000.0000.000
display_60.0000.0610.0000.0000.0000.0000.0630.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_70.0000.0300.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_90.1100.2650.0480.0470.0000.0460.0660.0230.0000.0000.0000.0000.0000.0001.0000.0280.0000.0000.0000.0000.0410.0000.0480.0000.0000.0140.0000.0220.0540.0240.0000.0130.0120.0000.0200.0460.0000.0000.0000.0000.0000.0000.0000.0050.0000.000
mailer_A0.0290.1040.2360.2500.0230.0230.0160.0050.0000.0730.0000.0780.0000.0000.0281.0000.0000.0000.0000.0000.0220.0000.0030.0110.0000.0130.0210.0000.0130.0140.0000.0000.0060.0000.0000.0300.0000.0180.0110.0000.0000.0000.0000.0070.0000.011
mailer_C0.0000.2410.1880.0230.1260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_F0.0090.0540.0400.0410.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_H0.0420.0670.0640.0640.0000.0200.0240.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0180.0000.0310.0020.0400.0000.0060.0090.0310.0290.0120.0070.0000.0000.0130.0260.0000.0000.0000.0000.0000.0000.0000.0090.0000.000
mailer_L0.0220.0950.0000.0000.0000.0060.0000.0000.0000.0430.0000.0260.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
marital_status_A0.3000.0750.1060.1030.0430.1710.3390.0760.0000.0000.0000.0000.0000.0000.0410.0220.0000.0090.0180.0001.0000.3840.4200.1830.1470.0540.0540.2390.2860.2410.2080.2480.1160.1290.1990.6390.0190.0730.0400.0210.0180.0480.0330.0290.0180.040
marital_status_B0.2070.0320.0300.0470.0120.2700.2260.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3841.0000.0820.0710.0550.3950.3000.1110.2350.1220.2610.0450.1730.0390.0810.2440.0490.0270.1020.0000.0270.0340.0630.0270.0270.102
homeowner_Homeowner0.3290.1120.1500.1480.0590.3150.4570.0180.0000.0000.0040.0240.0000.0040.0480.0030.0000.0000.0310.0000.4200.0821.0000.2680.1940.4040.3250.1650.3710.2390.0000.0330.1190.0580.0660.3290.0280.0150.0190.0410.0000.0250.0000.0160.0000.019
homeowner_Probable Owner0.5280.1030.1970.2000.0190.2840.2980.0480.0000.0000.0000.0070.0000.0000.0000.0110.0000.0000.0020.0000.1830.0710.2681.0000.0270.0610.4800.0850.0920.0720.0470.3470.0560.0460.2260.3330.0110.0910.0000.0110.0370.0050.0050.0470.0370.000
homeowner_Probable Renter0.2990.0520.1040.1010.0410.1050.2490.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.1470.0550.1940.0271.0000.0430.0450.0670.1130.1610.0290.0640.0400.0330.0930.2330.0000.0200.0130.0030.0110.0000.0000.0220.0110.013
homeowner_Renter0.3120.0870.1650.1640.0360.2600.2670.0880.0000.0050.0260.0160.0000.0000.0140.0130.0000.0000.0000.0000.0540.3950.4040.0610.0431.0000.3820.0420.2030.0670.0650.0690.2380.0870.2360.1970.0620.0220.0340.0110.0340.0030.0000.0320.0340.034
hhcomp_1 Adult Kids0.3470.1230.1620.1630.0380.2890.3340.0650.0000.0060.0000.0020.0000.0000.0000.0210.0000.0000.0060.0000.0540.3000.3250.4800.0450.3821.0000.1480.2450.1590.0740.3670.3370.0130.5000.4600.0000.0540.0200.0220.0240.0150.0000.0250.0240.020
hhcomp_2 Adults Kids0.2980.1230.1010.0930.0590.2660.3430.0800.0000.0000.0000.0230.0000.0000.0220.0000.0000.0000.0090.0000.2390.1110.1650.0850.0670.0420.1481.0000.3580.2330.1100.4490.2790.4180.7320.8070.0230.0230.0000.0000.0030.0090.0000.0150.0030.000
hhcomp_2 Adults No Kids0.3420.0680.0870.0870.0440.2300.3920.0330.0000.0000.0000.0000.0000.0000.0540.0130.0000.0030.0310.0000.2860.2350.3710.0920.1130.2030.2450.3581.0000.3860.1820.3390.2150.1810.4890.8550.0000.0090.0600.0420.0220.0380.0390.0000.0220.060
hhcomp_Single Female0.2750.0700.0920.0930.0300.1710.2860.0310.0000.0060.0000.0030.0000.0000.0240.0140.0000.0000.0290.0000.2410.1220.2390.0720.1610.0670.1590.2330.3861.0000.1180.2200.1400.1180.3180.5210.0000.0030.1210.0180.0250.0280.0060.0150.0250.121
hhcomp_Single Male0.1670.0500.0390.0780.0020.2670.2180.0530.0000.0290.0000.0000.0060.0000.0000.0000.0000.0000.0120.0000.2080.2610.0000.0470.0290.0650.0740.1100.1820.1181.0000.1040.0650.0550.1500.3440.0030.0240.0000.0050.0120.0720.0900.0200.0120.000
kid_category_10.2940.0390.0720.0710.0090.2740.2700.0120.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0070.0000.2480.0450.0330.3470.0640.0690.3670.4490.3390.2200.1041.0000.1230.1030.6920.8210.0000.0470.0100.0160.0190.0170.0000.0230.0190.010
kid_category_20.3150.0210.0110.0190.0270.3050.1820.0000.0000.0000.0000.0000.0000.0000.0120.0060.0000.0000.0000.0000.1160.1730.1190.0560.0400.2380.3370.2790.2150.1400.0650.1231.0000.0650.4400.7670.0110.0290.0310.0000.0320.0120.0130.0390.0320.031
kid_category_3+0.1750.1220.0560.0490.0350.2020.1350.0840.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.1290.0390.0580.0460.0330.0870.0130.4180.1810.1180.0550.1030.0651.0000.3710.9780.0490.0220.0250.0000.0350.0000.0000.0000.0350.025
kid_category_None/Unknown0.2950.0460.0420.0410.0280.3150.2940.0260.0000.0000.0000.0110.0000.0000.0200.0000.0000.0000.0130.0000.1990.0810.0660.2260.0930.2360.5000.7320.4890.3180.1500.6920.4400.3711.0000.9530.0190.0020.0190.0180.0130.0240.0030.0000.0130.019
hhsize_ordinal0.2710.0800.0660.0650.0350.1840.2140.1140.0000.0000.0080.0340.0000.0000.0460.0300.0000.0120.0260.0000.6390.2440.3290.3330.2330.1970.4600.8070.8550.5210.3440.8210.7670.9780.9531.0000.0540.0610.1080.0320.0880.0760.0540.0240.0880.108
campaign_8.00.0240.0830.0400.0330.0220.0780.0390.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0190.0490.0280.0110.0000.0620.0000.0230.0000.0000.0030.0000.0110.0490.0190.0541.0000.0040.0000.0000.0090.0000.0000.3630.0090.000
campaign_13.00.0870.0830.1860.1950.0000.0630.0330.0460.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0730.0270.0150.0910.0200.0220.0540.0230.0090.0030.0240.0470.0290.0220.0020.0610.0041.0000.0140.0050.0270.0000.0000.7270.0270.014
campaign_15.00.1680.0640.0990.0980.0000.2000.0800.0240.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0400.1020.0190.0000.0130.0340.0200.0000.0600.1210.0000.0100.0310.0250.0190.1080.0000.0141.0000.0000.0190.0000.0000.0220.0190.997
campaign_18.00.0490.1140.0960.0960.0280.0200.0540.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0410.0110.0030.0110.0220.0000.0420.0180.0050.0160.0000.0000.0180.0320.0000.0050.0001.0000.0100.0000.0000.3740.0100.000
campaign_24.00.0790.0280.0630.0460.1030.0740.0720.0560.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0270.0000.0370.0110.0340.0240.0030.0220.0250.0120.0190.0320.0350.0130.0880.0090.0270.0190.0101.0000.0020.0030.0390.9990.019
campaign_26.00.0790.0300.0550.0530.0000.0760.0480.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0340.0250.0050.0000.0030.0150.0090.0380.0280.0720.0170.0120.0000.0240.0760.0000.0000.0000.0000.0021.0000.0000.2840.0020.000
campaign_30.00.0630.0290.0550.1120.0000.0900.0620.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0630.0000.0050.0000.0000.0000.0000.0390.0060.0900.0000.0130.0000.0030.0540.0000.0000.0000.0000.0030.0001.0000.2870.0030.000
description_TypeA0.0960.0930.1160.1200.0000.0740.0660.0420.0000.0000.0000.0000.0000.0000.0050.0070.0000.0000.0090.0000.0290.0270.0160.0470.0220.0320.0250.0150.0000.0150.0200.0230.0390.0000.0000.0240.3630.7270.0220.3740.0390.2840.2871.0000.0390.022
description_TypeB0.0790.0280.0630.0460.1030.0740.0720.0560.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0270.0000.0370.0110.0340.0240.0030.0220.0250.0120.0190.0320.0350.0130.0880.0090.0270.0190.0100.9990.0020.0030.0391.0000.019
description_TypeC0.1680.0640.0990.0980.0000.2000.0800.0240.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0400.1020.0190.0000.0130.0340.0200.0000.0600.1210.0000.0100.0310.0250.0190.1080.0000.0140.9970.0000.0190.0000.0000.0220.0191.000

Missing values

2023-05-28T10:59:05.927248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-28T10:59:07.571958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Fmailer_Hmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_ordinalincome_ordinalhhsize_ordinalcampaign_8.0campaign_13.0campaign_15.0campaign_18.0campaign_24.0campaign_26.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
025True1.590.3710690.371069-0.000000000000000000001010000010000014.062.02.00000000000
126True1.290.2325580.232558-0.000000000000000000001010000010000014.062.02.00000000000
227True1.590.3710690.371069-0.000000000000000000001010000010000014.062.02.00000000000
328True2.490.000000-0.000000-0.000000000000000000001010000010000014.062.02.00000000000
429True2.990.1337790.133779-0.000000000000000000001010000010000014.062.02.00000000000
530True2.590.000000-0.000000-0.000000000000000000001010000010000014.062.02.00000000000
631True2.590.000000-0.000000-0.000000000000000000001010000010000014.062.02.00000000000
732False2.590.1930500.193050-0.000000000000000000001010000010000014.062.02.00000000000
833False2.590.000000-0.000000-0.000000000000000000001010000010000014.062.02.00000000000
934True12.490.2802240.2001600.080064000000000000000000000100010002.029.53.00000000000
Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Fmailer_Hmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_ordinalincome_ordinalhhsize_ordinalcampaign_8.0campaign_13.0campaign_15.0campaign_18.0campaign_24.0campaign_26.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
1481922217False11.890.0756940.075694-0.0000000000000000000000000100014.039.51.00000000000
1482022218False11.890.0756940.075694-0.0000000000000000000000000100014.039.51.00000000000
1482122219True5.990.000000-0.000000-0.0000000000000000000000000100014.039.51.00000000000
1482222220True3.590.4428970.442897-0.0000000000000000010000010000012.062.02.00000000000
1482322221True3.590.4428970.442897-0.0000000000100000010000010000012.062.02.00000000000
1482422222True4.290.2331000.233100-0.0000000000000000000000100010002.010.03.00000000000
1482522223True1.990.0502510.050251-0.0000000000000000000000100010002.010.03.00000000000
1482622224True2.990.1337790.133779-0.0000000000000000000000100010002.010.03.00000000000
1482722225False3.190.1880880.188088-0.0000000000000000000000100010002.010.03.00000000000
1482822226True2.990.0668900.066890-0.0000000000000000000000100010002.010.03.00000000000